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Research on optimization of an enterprise financial risk early warning method based on the DS-RF model

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  • Zhu, Weidong
  • Zhang, Tianjiao
  • Wu, Yong
  • Li, Shaorong
  • Li, Zhimin

Abstract

The financial risk early warning process of enterprises faces problems such as uncertainty and complexity. In the big data environment, scholars and enterprises that continue to use traditional evaluation methods will face large challenges. It is essential for an enterprise's sustainable operation to combine artificial intelligence algorithms, dynamically monitor its financial risks, and carry out financial risk early warning processes accurately and effectively. This study proposes an early warning method for corporate financial risks based on the evidence theory-random forest (DS-RF) model. The classic algorithm of machine learning—random forest was introduced into the framework of evidence theory to construct a random forest model with four dimensions: profitability, asset quality, debt risk, and operating growth. While predicting the risk, the credibility of the evidence was determined, and then the D-S synthesis rule was used for information fusion. An example was analyzed, taking JS Reclamation Group as the study subject. The comparison with the early warning results of the random forest algorithm and the traditional model shows that the DS-RF model proposed in this paper has a higher early warning accuracy and the results are presented more comprehensively and systematically, which effectively improves the efficiency of enterprise financial risk early warning and helps managers to make relevant decisions efficiently and scientifically.

Suggested Citation

  • Zhu, Weidong & Zhang, Tianjiao & Wu, Yong & Li, Shaorong & Li, Zhimin, 2022. "Research on optimization of an enterprise financial risk early warning method based on the DS-RF model," International Review of Financial Analysis, Elsevier, vol. 81(C).
    • Handle: RePEc:eee:finana:v:81:y:2022:i:c:s1057521922001077
    DOI: 10.1016/j.irfa.2022.102140
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    References listed on IDEAS

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    Cited by:

    1. Zhang, Tianjiao & Zhu, Weidong & Wu, Yong & Wu, Zihao & Zhang, Chao & Hu, Xue, 2023. "An explainable financial risk early warning model based on the DS-XGBoost model," Finance Research Letters, Elsevier, vol. 56(C).
    2. Xiangzhou Chen & Zhi Long, 2023. "E-Commerce Enterprises Financial Risk Prediction Based on FA-PSO-LSTM Neural Network Deep Learning Model," Sustainability, MDPI, vol. 15(7), pages 1-17, March.
    3. Ding, Shusheng & Cui, Tianxiang & Bellotti, Anthony Graham & Abedin, Mohammad Zoynul & Lucey, Brian, 2023. "The role of feature importance in predicting corporate financial distress in pre and post COVID periods: Evidence from China," International Review of Financial Analysis, Elsevier, vol. 90(C).
    4. Zhao, Zichao & Li, Dexuan & Dai, Wensheng, 2023. "Machine-learning-enabled intelligence computing for crisis management in small and medium-sized enterprises (SMEs)," Technological Forecasting and Social Change, Elsevier, vol. 191(C).
    5. Zhou, Ying & Shen, Long & Ballester, Laura, 2023. "A two-stage credit scoring model based on random forest: Evidence from Chinese small firms," International Review of Financial Analysis, Elsevier, vol. 89(C).
    6. Yuanying Chi & Mingjian Yan & Yuexia Pang & Hongbo Lei, 2022. "Financial Risk Assessment of Photovoltaic Industry Listed Companies Based on Text Mining," Sustainability, MDPI, vol. 14(19), pages 1-17, September.

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